vaccines and immunoinformatics approaches

T-CoV: a comprehensive portal of HLA-peptide interactions affected by SARS-CoV-2 mutations

T-CoV analyzes how SARS-CoV-2 mutations affect HLA-peptide interactions to predict variant-specific immune evasion.

EpitopeVec: Linear Epitope Prediction Using DeepProtein Sequence Embeddings

EpitopeVec improves linear epitope prediction using deep protein sequence embeddings, achieving better accuracy and AUC than existing methods.

Designing a Novel Multi-Epitope Vaccine against SARS-CoV-2; Implication for Viral Binds and Fusion Inhibition through Inducing Neutralizing Antibodies

The paper designs a multi-epitope vaccine candidate against SARS-CoV-2 by analyzing the spike glycoprotein's immunogenic properties and predicting B-cell epitopes.

clusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences

clusTCR is a Python tool for fast clustering of large CDR3 sequences, offering improved speed over existing methods while maintaining comparable accuracy.

Epitope-Based Peptide prediction of vaccine against European bat lyssaviruses type 2 Glycoprotein G with Immunoinformatic Approach

The paper predicts epitope-based peptides for a vaccine against European bat lyssavirus type 2 using immunoinformatic methods and identifies a highly conserved epitope with broad population coverage.

BERTMHC: Improves MHC-peptide class II interaction prediction with transformer and multiple instance learning

BERTMHC improves MHC class II peptide binding prediction using a transformer model and multiple instance learning, outperforming existing methods.

NAP-CNB: Bioinformatic pipeline to predict MHC-I-restricted T cell epitopes in mice

NAP-CNB is a web server tool that predicts MHC-I-restricted T cell epitopes in mice using recurrent neural networks, achieving an AUC of 0.95 for H-2 peptide ligands.

A Facile Immunopeptidomics Workflow for Capturing the HLA-I Ligandome with PEAKS XPro

This paper introduces an immunopeptidomics workflow using PEAKS XPro to identify HLA-I ligands and neoantigens without relying on standard databases, revealing diverse peptides and mutations in colorectal cancer.

Flexibility-aware graph-based algorithm improves antigen epitopes identification

This paper introduces a flexibility-aware graph-based model to identify antigen epitopes, achieving an F1-score of 0.656 and improving upon existing methods by 16.3%.

Epitope-Based Peptide Vaccine Design Against Fructose Bisphosphate Aldolase of Candida Glabrata: An Immunomics Approach

The paper predicts B- and T-cell epitopes from the Fba1 protein of Candida glabrata using in silico tools to design an epitope-based vaccine.

SeRenDIP-CE: Sequence-based Interface Prediction for Conformational Epitopes

The paper introduces SeRenDIP-CE, a sequence-based method for predicting conformational epitopes, achieving better performance than existing predictors on an antibody-antigen structure.

DeepSeqPanII: an interpretable recurrent neural network model with attention mechanism for peptide-HLA class II binding prediction

DeepSeqPanII predicts peptide-HLA class II binding using an interpretable recurrent neural network with attention mechanisms, achieving state-of-the-art performance.

Predicting TCR-epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

This paper predicts TCR-epitope binding specificity using deep metric learning and multimodal learning, revealing key amino acid patterns and challenging assumptions about physical proximity.

Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

The paper identifies SARS-CoV-2 low complexity regions (LCRs) that are similar to human proteins, suggesting potential risks in vaccine design targeting these regions.

DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T cell immunity

DeepImmuno predicts and generates immunogenic peptides using deep learning models, outperforming existing methods and providing insights into T cell antigen recognition.

An immunoinformatics approach to study the epitopes contributed by Nsp13 of SARS-CoV-2

This study identifies and characterizes B cell and T cell epitopes in SARS-CoV-2 Nsp13, revealing mutations that may impact antigenicity and immune recognition.

Integral use of immunopeptidomics and immunoinformatics for the characterization of antigen presentation and rational identification of BoLA-DR-presented peptides and epitopes

The paper integrates immunopeptidomics and immunoinformatics to characterize BoLA-DR antigen presentation and identify CD4 T cell epitopes with high predictive accuracy.

Immunoinformatic approach to design a vaccine against SARS-COV-2 membrane glycoprotein

The paper designs a multiepitope vaccine against SARS-CoV-2 membrane protein using immunoinformatic methods and identifies an epitope with strong binding to HLA-A and HLA-B alleles.

AI aided design of epitope-based vaccine for the induction of cellular immune responses against SARS-CoV-2

This paper uses AI and bioinformatics to design epitope-based vaccines for cellular immunity against SARS-CoV-2, evaluating immunogenicity and safety risks.

Machine learning prediction of Antibody-Antigen binding: dataset, method and testing

The paper develops a machine learning method to predict antibody-antigen binding using sequence data and evaluates it on a dataset of 600 antibodies and 50 antigens, achieving 76% classification accuracy.

Immunoinformatic Approach for the identification of T Cell and B Cell Epitopes in the Surface Glycoprotein and Designing a Potent Multiepitope Vaccine Construct Against SARS-CoV-2 including the new UK variant

This study identifies T and B cell epitopes in SARS-CoV-2 Spike protein and designs a multiepitope vaccine construct targeting the UK variant, showing its potential to elicit immune responses.

Linear B-cell epitope prediction for in silico vaccine design: a performance review of methods available via command-line interface

This paper reviews linear B-cell epitope prediction methods available via command-line interface and introduces a consensus classifier to improve performance for vaccine design.

Vaccinomics Approach for Scheming Potential Epitope-Based Peptide Vaccine by Targeting L-protein of Marburg Virus

This paper designs a peptide vaccine targeting the L-protein of Marburg virus using vaccinomics, identifying four epitopes with high conservancy and immunogenic potential.

epitopepredict: A tool for integrated MHC binding prediction

epitopepredict is a tool that integrates multiple MHC binding prediction algorithms to identify potential T cell epitopes from whole genomes.

In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19

The paper predicts SARS-CoV-2 B-cell epitopes using computational methods and validates them with ELISA, finding two antigens with high sensitivity and specificity for serological diagnosis of COVID-19.

Designing of multiepitope-based vaccine against Leptospirosis using Immuno-Informatics approaches.

This paper designs a multiepitope vaccine against Leptospirosis using immunoinformatics, identifying 6 vaccine candidates and constructing a stable vaccine with strong immune simulation results.

HLA binding of self-peptides is biased towards proteins with specific molecular functions

The paper shows HLA alleles preferentially present peptides from proteins with specific molecular functions, linking these preferences to HLA allele differences and disease susceptibility.

Codon arrangement modulates MHC-I peptides presentation: implications for a SARS-CoV-2 peptide-based vaccine

The paper uses CAMAP to predict SARS-CoV-2 MHC-I peptides, identifies 48 vaccine candidates covering most HLA haplotypes and strains, and provides an interactive portal for exploration.

Learning the language of viral evolution and escape

The paper uses neural language models to predict viral escape mutations by modeling semantic change and grammaticality in viral sequences.

In silico design of Multi-epitope-based peptide vaccine against SARS-CoV-2 using its spike protein

The paper designs a multi-epitope peptide vaccine against SARS-CoV-2 using spike protein epitopes and MD simulations, showing potential for TLR4 docking.